The Raman Effect is the inelastic scattering of light by a sample. In Raman Spectroscopy, a sample is irradiated by monochromatic laser light and the scattered light is then dispersed by a dispersive device, such as a diffraction grating, e.g. in a monochromator, to generate a spectrum called a Raman spectrum. The Raman spectrum is detected by a detector such as a charge-coupled device (CCD). Examples of Raman spectroscopy apparatus are known from U.S. Pat. Nos. 5,442,438 and 5,510,894, which are incorporated herein by reference.
Different chemical compounds have different characteristic Raman spectra. Accordingly, the Raman effect can be used to analyse chemical compounds present in a sample.
The detected spectrum comprises the Raman spectrum together with a background signal whose intensity, particularly for biological samples, is orders of magnitude greater than the Raman spectrum. This background signal is typically due to, amongst other things, the substrate supporting the sample, fluorescence and an objective lens of the Raman apparatus. To analyse the Raman spectrum it is often first necessary to identify a proportion of the detected spectrum that can be attributed to background sources.
B. D. Beier and A. J. Berger, The Royal Society of Chemistry, 2009, 134, 1198-1202 discloses a method for automating the removal of background from a Raman signal using a polynomial fitting technique and a reference spectrum of a known spectral contaminant. In the example described, glass of a microscope slide is the known contaminant.
The method comprises an iterative algorithm wherein, to start with, an estimate of the background component is set as the detected spectrum. An initial estimate is made of the concentration of the known contaminant and a polynomial is fitted to the residual between the estimated background and the estimated contribution made by the known contaminant. The polynomial and the estimated contribution of the known contaminant form together a current estimate of the background. A new estimate of the background for the next iteration is determined by comparing the current estimate to the previous estimate of the background and retaining the minimum value at each wavenumber.
It is desirable to have a technique for automatically estimating the background that does not require knowledge of spectral components that contribute to the background.
Automated Autofluorescence Background Subtraction Algorithm for Biomedical Raman Spectroscopy, Jianhua Zhao, Harvey Lui, David I. McLean and Haishan Zeng, Applied Spectroscopy, Volume 61, number 11, 2007, pages 1225-1232, discloses an iterative method of estimating fluorescence background comprising fitting a polynomial to a spectrum. In the first iteration, the polynomial is fitted to the raw Raman spectrum. In each successive iteration, a polynomial is fitted to a modified spectrum. In the first iteration, the modified spectrum is formed using a peak removal step, wherein, for each wavenumber, the lower of a value equal to the polynomial plus a value DEV and the raw data value is retained. DEV is the standard deviation of a residual component the remains when the polynomial is taken away from the raw Raman data. For subsequent iterations, the modified spectrum is formed by retaining the lower value of the polynomial and the modified spectrum to which the polynomial was fitted.
Other methods of estimating fluorescence background wherein a polynomial is iteratively fitted to Raman data are disclosed in Automated Method for Subtraction of Fluorescence from Biological Raman Spectra, Chad A. Lieber and Anita Mahadevan-Jensen, Applied Spectroscopy, Volume 57, Number 11, 2003, pages 1363 to 1367 and Baseline Correction By Improved Iterative Polynomial Fitting with Automated Threshold, Feng Gan, Guihua Ruan, Jinyuan Mo, Chemometrics and intelligent Laboratory Systems, 82 (2006), pages 59-65.